Responses of drinking water bulk and biofilm microbiota to elevated water age in bench-scale simulated distribution systems

Reductions in nonresidential water demand during the COVID-19 pandemic highlighted the importance of understanding how water age impacts drinking water quality and microbiota in piped distribution systems. Using benchtop model distribution systems, we aimed to characterize the impacts of elevated water age on microbiota in bulk water and pipe wall biofilms. Five replicate constant-flow reactors were fed with municipal chloraminated tap water for 6 months prior to building closures and 7 months after. After building closures, chloramine levels entering the reactors dropped; in the reactor bulk water and biofilms the mean cell counts and ATP concentrations increased over an order of magnitude while the detection of opportunistic pathogens remained low. Water age, and the corresponding physicochemical changes, strongly influenced microbial abundance and community composition. Differential initial microbial colonization also had a lasting influence on microbial communities in each reactor (i.e., historical contingency).

Section 2. Variability of bulk water and biofilm parameters across annular reactors Mean arithmetic coefficient of variation for total chlorine across the five reactors on each sampling day was 48.9%.Mean geometric coefficient of variation for ICC across the five reactors on each sampling day was 89.9%.For biofilm samples, ICC was not statistically different by reactor (Kruskal-Wallis, p=0.90).
On the final biofilm sampling day, three slides were sampled from reactor 3 and independently analyzed to compare variability at different scales of measurement.At the smallest scale, the average coefficient of variation of biofilm height in analyzing different image fields of the same biofilm slide was 74%, representing a high level of small-scale heterogeneity.Across the three slide replicates from the same reactor, the coefficient of variation of mean heights was lower, at 19.8%.Across the five reactors, the coefficient of variation in the means of each reactor height measured on the same day was 50%.A similar analysis was conducted for the same reactor 3 biofilm samples from day 387 with ICC.The mean geometric coefficient of variation of flow cytometry technical replicates was 106%, while the geometric coefficient of variation in ICC across the three slide replicates was 80.5%.The coefficient of variation in the means of biofilm ICC measured on the same day across the five reactors was only 23.2%.
Section 3. Quantification of nitrifying genes One functional gene target was measured via qPCR: the nitrifying gene amoA.While multiple detected MAGs were identified as nitrifiers, out of 34 samples, only one sample (AR2_134) had a quantifiable concentration of amoA of 87.7 gc/L, while the theoretical limit of detection and quantification for a reactor sample was 60 gc/L.Using metagenomic data, the concentration of pmoA/amoA in this sample was approximated as 45.2 gc/L by summing the relative abundance of bins containing pmoA/amoA (assuming one copy per bin) and multiplying by TCC.This approximation is close to the concentration measured via qPCR; however, estimates based on metagenomic data from ten other bulk water samples exceeded pmoA/amoA estimates from AR2_134, with some concentrations as high as 1150 gc/L.Six genes were identified as amoA/pmoA.Of these, the amoA qPCR assay primers matched to two of them (identified as >6 matching bases and <3 mismatches) although none matched fully without mismatches, which may further account for low detection via qPCR with this amoA assay.Estimates based on metagenomic data were likely higher because of the inclusion of pmoA along with amoA and because estimated concentrations based on metagenomic data and flow cytometry may not be as susceptible to extraction losses and sample-specific inhibition as qPCR.Given that we did not distinguish between amoA and pmoA in metagenomic data, it is not surprising that amoA-specific qPCR produced only one detection even though the genes were identified in multiple samples via metagenomics.
entering the reactor through advective flow from the tap reservoir and cells leaving the reactor through advective flow.Supplementary Figure 7: Time Series of Tap Reservoir Cell Counts and ATP.Tap reservoir total/intact cell counts and total/intracellular ATP concentrations over time.Supplementary Figure 8: Boxplots of bulk water quality parameters.Includes cell counts and ATP concentrations in Phase I versus Phase II in both the tap reservoir and the reactors.Box edges correspond to the first quartile, median, and third quartile.Whiskers extend from the edge to the largest and smallest values no further than 1.5× the interquartile range from the edge.Data beyond the whiskers are considered outliers and plotted individually.Supplementary Figure 9: Cell Counts vs. Water Usage.ICC in the reactor bulk water and tap reservoir versus monthly building water use based on the shared building water meter.Supplementary Figure10: Biofilm Cell Fraction Time Series.Intact cells in the biofilm were calculated by multiplying the ICC per area by the total area of PVC coupons in the reactor.Intact cells in the bulk water were calculated by multiplying the concentration by the reactor volume.Total cells were calculated by adding together the intact cells in the biofilm and intact cells in the bulk water.Parameters of Biofilm Images.Output parameters of biofilm over time from quantitative image analysis of fluorescent confocal laser scanning microscopy images.Parameters were averaged across all images captured for a single sample.Extracellular polymeric substance (EPS) parameters were based on signal from SYPRO Orange and ConA.Height of cells and eDNA was based on signal from SYTO9 dye.Units of height, area, and volume are in μm, μm 2 , and μm 3 , respectively.Box edges correspond to the first quartile, median, and third quartile.Whiskers extend from the edge to the largest and smallest values no further than 1.5× the interquartile range from the edge.Data beyond the whiskers are considered outliers and plotted individually.

Figure 23: Heatmap of All MAGs.
Heatmap of the normalized relative abundance (log10) of all the dereplicated bins across all sequenced samples and negative controls.
Supplementary Figure22: Enriched MAGs Phase II vs. Phase I. Enriched MAGs in the AR bulk water between Phase I (log2FoldChange < 0) and Phase II (log2FoldChange > 0) based on differential abundance analysis with DESeq2.Sample AR4_162 was not included in the analysis because of suspected contamination.

Supplementary Figure 24: RDA Plot of Phase II. Ordination
of RDA of reactor bulk and biofilm samples from Phase II only.

Table 3 : qPCR reaction concentrations and volumes.
Nuclease-free water was added to each reaction to bring the total volume up to 20 μL (amoA) or 10 μL (all other assays) after the addition of other reagents.

Table 5 :
qPCR plate quality.Summarizes standard curve parameters and NTCs Supplementary Table 6: Limits of detection and quantification per assay.Limit of detection (LoD) was defined as the interpolated copy number at which 95% of replicates amplified.Limit of quantification (LoQ) was defined as the interpolated copy number at which the coefficient of variation equalled 35%.If the coefficient of variation never exceeded 35% or went below 35% at a value less than the LoD, the LoQ was set to the LoD.

Table 7 :
qPCR Inhibition Testing.A distribution of samples across reactors and time points were selected for inhibition testing.For each assay, two biofilm and two bulk water samples were selected: one with high DNA concentration based on Qubit readings and one with low.Samples at each dilution were considered inhibited if dCt relative to the previous dilution was greater than one.Only one sample showed inhibition at any dilution (oprL assay, AR4_372) which was diluted out at 4x.

Table 8 :
Sequencing Quality by Sample.Information for each Sequenced Sample and Control